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Explainable Network and AI-based methods for personalized multi-omic medical data analysis


   Barts and The London School of Medicine and Dentistry

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  Dr Oleg Blyuss, Prof S Duffy, Dr A Brentnall  No more applications being accepted  Funded PhD Project (UK Students Only)

About the Project

Applications are invited from graduates with a BSc (First or Upper Second) or MSc (Merit or Distinction), or equivalent, to work within the Wolfson Institute of Population Health (https://www.qmul.ac.uk/wiph/). This 4-year studentship will commence in September 2022 (or January 2023 if preferred) and will be based at the Charterhouse Square Campus. This is an exciting opportunity for a graduate from disciplines related to computational/applied mathematics, informatics or statistics.

 Project description

 Introduction

Recent technological developments allow collection of a tremendous amount of digital personal multi-omic medical data that includes clinical parameters, serum and urine biomarkers, or genomic data. Some of these analytes are collected over time and organise serial or longitudinal data. Resulting datasets are frequently characterized by small sample size and large number of features. This results in that fact that existing machine learning approaches often fail to analyse such data and are prone to overfitting.

One of the ways to analyse multiple features from patients in such a setting is to use network approach such as parenclitic networks1-3. Although it was shown to be promising in analysing low sample size multidimensional datasets, there are still unsolved methodological issues which preclude successful implementation of this group of methods.

Research aims and objectives

The main aim of this research is to investigate the opportunities that network-based approaches provide for modelling the complex data and how these could be utilized in multi-omic medical setting.

The specific research objectives are:

1)    To review existing network approaches available for predictive modelling purposes.

2)    To investigate which topological indices are most relevant for parenclitic networks analysis.

3)    To investigate how to incorporate longitudinal characteristics into the network setting.

4)    To investigate how to make these models transparent and explainable.

This latter is crucial to be able to interpret the results of such black box approach and identify which particular patterns in the data have led to a specific classification result.

This research will address a number of methodological issues which relate to the application of network analysis approaches to predictive modelling. Findings would be particularly important in the context of the analysis of complex multi-omic longitudinal medical data. 

Informal enquiries can be made via email to:

Dr Oleg Blyuss, Wolfson Institute of Population Health, [Email Address Removed]

Professor Stephen Duffy, Wolfson Institute of Population Health, [Email Address Removed]

Dr Adam Brentnall, Wolfson Institute of Population Health, [Email Address Removed]

How to apply

Your application should consist of a CV and contact details of two academic referees. You must also include a personal statement (1,000 words maximum) describing your suitability for the selected project including how your research experience and interests relate to the project.

Please submit your application to: Patrick Mullan ([Email Address Removed]). Successfully shortlisted candidates will be invited to an interview.


Funding Notes

This 4-year PhD studentship is funded by Barts Charity and comes with a tax-free stipend of £24,278. It is open to UK Nationals, and EU nationals that have EU Settlement Status and have been ordinarily resident in the UK or EEA for three years. University tuition fees (at Home rate) will be met by the funding body.

References

1. Karsakov A, Bartlett T, Meyerov I, Ivanchenko M, Zaikin A. (2017). Parenclitic networks analysis of methylation data for cancer identification. PLOS One 12(1): e0169661.
2. Whitwell HJ, Blyuss O, Timms, JF, Zaikin A. (2018). Parenclitic networks for predicting ovarian cancer. Oncotarget 9(32): 22717-22726.
3. Demichev V, Tober-Lau P, Nazarenko T, Thibeault C, Whitwell HJ, et al. (2020). A time-resolved proteomic and diagnostic map characterizes COVID-19 disease progression and predicts outcome. Cell systems 12(8): 780-794.

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